This study demonstrates the application of supervised machine learning (ML) techniques to distinguish between isotropic and jet-like event topologies in heavy-ion collisions via the spherocity observable. State-of-the-art ML algorithms, optimized through systematic hyperparameter tuning, are employed to predict both traditional transverse spherocity S0 and unweighted transverse spherocity S0pT=1 directly from raw event data. Moreover, the results from this study demonstrated that our approach remains largely model-independent, underscoring its potential applicability in future experimental heavy-ion physics analyses.
@article{arxiv.2508.13349,
title = {Harnessing data-driven methods for precise model independent event shape estimation in relativistic heavy-ion collisions},
author = {Dipankar Basak and H. Hushnud and Kalyan Dey},
journal= {arXiv preprint arXiv:2508.13349},
year = {2025}
}